Cowlonialism : Colonialism, cattle and landscapes in 16th century New Spain
Bibliographic record
Abstract
Cattle are not endemic to the American continent. Nevertheless, they are present and thrive in many landscapes, all the way from Canada to Argentina. The narratives about the process of colonisation of the American continent include human actors, but there is very little literature in comparison that deals on the influence of cattle in landscapes in the continent. In this thesis, I will contribute to the discussion about more-than-human processes of landscape modification, by analysing archival sources from the New Spain. This region included a big part of the West of the United States, Mexico and Central America. The period I analyse, between 1550 and 1602, represents the first decades of encounter between the Spanish settlers and indigenous communities, in the region of New Spain, where the Spanish established administrative institutions to manage their empire. The documents that I analysed showcase the transformations that cattle caused in the landscape, from how indigenous people lived, to what plants and crops could be cultivated. Inspired by Multi-species studies, ethography, and the concepts of “animal” and “landscape”, I use Actor-Network Theory to create a thoroughly described network of relations. In my analysis, I find that cattle influenced the activities that were performed in the landscape, as well as the ways that other actors interacted with each other. These actions, complemented by religious, economic and cultural ideas that circulated during the XVI century, would form what I call Cowlonialism, a regime of ideas and practices where cattle invade the land and displace their inhabitants, exercising power over other actors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".